Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


In this chapter, we expanded on the initial discussion of out-of-core algorithms by adding SVMs to simple regression-based linear models. Most of the time, we focused on Scikit-learn implementations—mostly SGD—and concluded with an overview of external tools that can be integrated with Python scripts, such as Vowpal Wabbit by John Langford. Along the way, we completed our overview on model improvement and validation technicalities when working out-of-core by discussing reservoir sampling, regularization, explicit and implicit nonlinear transformations, and hyperparameter optimization.

In the next chapter, we will get involved with even more complex and powerful learning approaches while presenting deep learning and neural networks in large scale problems. If your projects revolve around the analysis of images and sounds, what we have seen so far may not yet be the magic solution you were looking for. The next chapter will provide all the desired solutions.